Development of bioinformatics and multi-omics analyses in organoids
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Pre-clinical models are critical in gaining mechanistic and biologicalinsights into disease progression. Recently, patient-derivedorganoid models have been developed to facilitate our understandingof disease development and to improve the discoveryof therapeutic options by faithfully recapitulating in vivo tissuesor organs. As technological developments of organoid modelsare rapidly growing, computational methods are gaining attentionin organoid researchers to improve the ability to systematicallyanalyze experimental results. In this review, we summarize therecent advances in organoid models to recapitulate human diseasesand computational advancements to analyze experimentalresults from organoids.
Abstract
Pre-clinical models are critical in gaining mechanistic and biologicalinsights into disease progression. Recently, patient-derivedorganoid models have been developed to facilitate our understandingof disease development and to improve the discoveryof therapeutic options by faithfully recapitulating in vivo tissuesor organs. As technological developments of organoid modelsare rapidly growing, computational methods are gaining attentionin organoid researchers to improve the ability to systematicallyanalyze experimental results. In this review, we summarize therecent advances in organoid models to recapitulate human diseasesand computational advancements to analyze experimentalresults from organoids.
참고문헌 (54)
- Li X, "Oncogenic transformation of diverse gastrointestinal tissues in primary organoid culture", Nat Med, 20, pp.769~777 (2014)
- Artegiani B, "Probing the tumor suppressor function of BAP1 in CRISPR-engineered human liver organoids", Cell Stem Cell, 24, pp.927~943 (2019)
- Ringel T, "Genome-scale CRISPR screening in human intestinal organoids identifies drivers of TGF-β resistance", Cell Stem Cell, 26, pp.431~440 (2020)
- Michels BE, "Pooled in vitro and in vivo CRISPR-Cas9 screening identifies tumor suppressors in human colon organoids", Cell Stem Cell, 26, pp.782~792 (2020)
- Takeda H, "CRISPRCas9-mediated gene knockout in intestinal tumor organoids provides functional validation for colorectal cancer driver genes", Proc Natl Acad Sci U S A, 116, pp.15635~15644 (2019)
- Ungricht R, "Genome-wide screening in human kidney organoids identifies developmental and disease-related aspects of nephrogenesis", Cell Stem Cell, 29, pp.160~175 (2022)
- Beumer J, "A CRISPR", Cas9 genetically engineered organoid biobank reveals essential host factors for coronaviruses, 2021, pp.12, 5498~
- Schwank G, "Functional repair of CFTR by CRISPR", Cas9 in intestinal stem cell organoids of cystic fibrosis patients, 2013, pp.13, 653~658
- Khurana E, "Integrative annotation of variants from 1092 humans : application to cancer genomics", Science, 342, pp.1235587~ (2013)
- Ng PC, "Predicting deleterious amino acid substitutions", Genome Res, 11, pp.863~874 (2001)
- Kim D, "Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites", Nucleic Acids Res, 47, pp.e94~ (2019)
- Adzhubei IA, "A method and server for predicting damaging missense mutations", Nat Methods, 7, pp.248~249 (2010)
- Desmet FO, "Human splicing finder : an online bioinformatics tool to predict splicing signals", Nucleic Acids Res, 37, pp.e67~ (2009)
- Arnold F, "RINT1 regulates SUMOylation and the DNA damage response to preserve cellular homeostasis in pancreatic cancer", Cancer Res, 81, pp.1758~1774 (2021)
- Schadt EE, "An integrative genomics approach to infer causal associations between gene expression and disease", Nat Genet, 37, pp.710~717 (2005)
- Chen Y, "Variations in DNA elucidate molecular networks that cause disease", Nature, 452, pp.429~435 (2008)
- Cowan CS, "Cell types of the human retina and its organoids at single-cell resolution", Cell, 182, pp.1623~1640 (2020)
- Kanton S, "Organoid singlecell genomic atlas uncovers human-specific features of brain development", Nature, 574, pp.418~422 (2019)
- Kathuria A, "Transcriptome analysis and functional characterization of cerebral organoids in bipolar disorder", Genome Med, 12, pp.34~ (2020)
- Liu K, "Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data", Nat Commun, 10, pp.2138~ (2019)
- Krieger TG, "Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy", Nat Commun, 12, pp.5826~ (2021)
- Norkin M, "Highcontent, targeted RNA-seq screening in organoids for drug discovery in colorectal cancer", Cell Rep, 35, pp.109026~ (2021)
- Raspe E, "Gene expression profiling to dissect the complexity of cancer biology : Pitfalls and promise", Semin Cancer Biol, 22, pp.250~260 (2012)
- Menche J, "Uncovering disease-disease relationships through the incomplete interactome", Science, 347, pp.1257601~ (2015)
- Hofree M, "Network-based stratification of tumor mutations", Nat Methods, 10, pp.1108~1115 (2013)
- Kong JH, "Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients", Nat Commun, 11, pp.5485~ (2020)
- Amiri A, "Transcriptome and epigenome landscape of human cortical development modeled in organoids", Science, 362, pp.eaat6720~ (2018)
- Gordon A, "Long-term maturation of human cortical organoids matches key early postnatal transitions", Nat Neurosci, 24, pp.331~342 (2021)
- Trevino AE, "Chromatin accessibility dynamics in a model of human forebrain development", Science, 367, pp.eaay1645~ (2020)
- Broutier L, "Human primary liver cancer-derived organoid cultures for disease modeling and drug screening", Nat Med, 23, pp.1424~1435 (2017)
- Gao D, "Organoid cultures derived from patients with advanced prostate cancer", Cell, 159, pp.176~187 (2014)
- Boj SF, "Organoid models of human and mouse ductal pancreatic cancer", Cell, 160, pp.324~338 (2015)
- Codrich M, "Integrated multiomics analyses on patient-derived CRC organoids highlight altered molecular pathways in colorectal cancer progression involving PTEN", J Exp Clin Cancer Res, 40, pp.198~ (2021)
- Toshimitsu K, "Organoid screening reveals epigenetic vulnerabilities in human colorectal cancer", Nat Chem Biol, 18, pp.605~614 (2022)
- Chen J, "An organoid-based drug screening identified a menin-MLL inhibitor for endometrial cancer through regulating the HIF pathway", Cancer Gene Ther, 28, pp.112~125 (2021)
- Yan HHN, "A comprehensive human gastric cancer organoid biobank captures tumor subtype heterogeneity and enables therapeutic screening", Cell Stem Cell, 23, pp.882~897e11 (2018)
- Schütte M, "Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors", Nat Commun, 8, pp.14262~ (2017)
- Schumacher D, "Heterogeneous pathway activation and drug response modelled in colorectal-tumor-derived 3D cultures", PLoS Genet, 15, pp.e1008076~ (2019)
- Mills RJ, "Drug screening in human PSC-cardiac organoids identifies proproliferative compounds acting via the mevalonate pathway", Cell Stem Cell, 24, pp.895~907e6 (2019)
- Webber JT, "Integration of tumor genomic data with cell lines using multi-dimensional network modules improves cancer pharmacogenomics", Cell Syst, 7, pp.526~536e6 (2018)
- Han SK, "Exomic and transcriptomic alterations of hereditary gingival fibromatosis", Oral Dis, 25, pp.1374~1383 (2019)
- Sharifi-Noghabi H, "MOLI : multi-omics late integration with deep neural networks for drug response prediction", Bioinformatics, 35, pp.i501~i509 (2019)
- Boutros M, "Microscopybased high-content screening", Cell, 163, pp.1314~1325 (2015)
- Dekkers JF, "High-resolution 3D imaging of fixed and cleared organoids", Nat Protoc, 14, pp.1756~1771 (2019)
- Lukonin I, "Phenotypic landscape of intestinal organoid regeneration", Nature, 586, pp.275~280 (2020)
- Renner H, "A fully automated high-throughput workflow for 3d-based chemical screening in human midbrain organoids", Elife, 9, pp.e52904~ (2020)
- Renner H, "Combining automated organoid workflows with artificial intelligencebased analyses : opportunities to build a new generation of interdisciplinary high-throughput screens for Parkinson’s disease and beyond", Mov Disord, 36, pp.2745~2762 (2021)
- Gritti N, "Morgana : accessible quantitative analysis of organoids with machine learning", Development, 148, pp.dev199611~ (2021)
- Kassis T, "OrgaQuant : human intestinal organoid localization and quantification using deep convolutional neural networks", Sci Rep, 9, pp.12479~ (2019)
- Kok RNU, "OrganoidTracker : efficient cell tracking using machine learning and manual error correction", PLoS One, 15, pp.e0240802~ (2020)
- Kim E, "Creation of bladder assembloids mimicking tissue regeneration and cancer", Nature, 588, pp.664~669 (2020)
- Neal JT, "Organoid modeling of the tumor immune microenvironment", Cell, 175, pp.1972~1988e16 (2018)
- Han SK, "Divergence of noncoding regulatory elements explains genephenotype differences between human and mouse orthologous genes", Mol Biol Evol, 35, pp.1653~1667 (2018)
- Ha D, "Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes", Nucleic Acids Res, 50, pp.1849~1863 (2022)
